SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)

SAMStyler: Enhancing Visual Creativity With Neural Style Transfer and Segment Anything Model (SAM)

Neural Style Transfer (NST) is a popular technique of computer vision where the content of an image is blended with the style of another, which results in a fused image with certain properties of both original images. This approach has practical applications in various domains and has garnered signi...

תיאור מלא

מידע ביבליוגרפי
Main Authors: Konstantinos Psychogyios, Helen C. Leligou, Filisia Melissari, Stavroula Bourou, Zacharias Anastasakis, Theodore Zahariadis
פורמט: Article
שפה:English
יצא לאור: IEEE 2023-01-01
סדרה:IEEE Access
נושאים:
Segment anything model
segment anything
segmentation
machine learning
style transfer
גישה מקוונת:https://ieeexplore.ieee.org/document/10250775/

אינטרנט

https://ieeexplore.ieee.org/document/10250775/

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